This is a sample presentation for showing progress in Bachelor in Science thesis.

1. Seminar
MTE 4110
Heaven’s light is our guide
Rajshahi University of Engineering & Technology
Prepared by:
Md. Mahfuz Rayhan
Roll: 1708027
Md. Junayed
Roll: 1708037
Supervised by:
Md. Mehedi Hasan
Lecturer,
Department of Mechatronics Engineering, RUET
Real Time Recognition of Ambulation Mode in Transfemoral Prostheses.

2. 13/12/2022 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 2
Components to be Used for Data Acquisition
• IMU (Adafruit BNO055)
• Pressure Sensor (Piezo-
electric)
Sensors
• ESP 32 NodeMCU
Data
Transmitting
Device

3. 13/12/2022 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 3
Cost Estimation
Item Name Item Quantity Per Item Cost Sub Total
IMU- Adafruit-BNO055 3 2000 6000
Pressure Sensor 2 100 200
ESP 32 NodeMCU 1 1200 1200
Utilities 1000
Total 8400

4. 13/12/2022 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 4
Data Acquisition Process
Sensors
• IMU
• Pressure
Sensor
Data
Transmitting
Device
• ESP 32
NodeMCU
Data Storage
• Excel Sheet

5. Sensor Positioning
IMU
• Above knee
joint
• Below knee
joint
• Ankle
Piezo-electric
• Heel of the
foot
• Toe (Hallux)
Real Time Recognition of Ambulation Mode in Transfemoral Prostheses.
13/12/2022
Figure 1: Sensor Positions

6. 1/12/2023 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 6
Dataset Description
Mechanical Locomotion Data:
• IMU sensors to use for collecting data.
• Sensors placed at thigh, shank and foot.
• 6 Channels per IMU: 3-axis gyration and 3-axis acceleration.
• Cycle Definition: Toe Off to Heel Contact
• Features: Minimum, Maximum, Mean, Median, Standard
Deviation of cycles for every channels.
• 3 IMUs × 6 Channels × 5 Features = 90 Features
• Sampling Rate: 100 Hz
Figure 01: Ambulation Circuit.
Locomotion modes:
• Plain Surface
• Upward Ramp
• Downward Ramp
• Upward Stair and
• Downward Stair
Figure 01: Cycle Definition.

7. 1/12/2023 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 7
Methodology
• Collect data as per dataset description and perform
• Post processing to remove anomalies, out of class ambulation
like tripping and such moves.
• Sample into cycles.
• Calculate features.
• Prepare a csv file for training purpose.
Data
Collection
• Perform all algorithms found in literature review to compare
results with. Those be: LDA, ANN, DT, RF, TH, SVM, DBN
etc and verify the results using ANOVA.
• Perform classification on other available datasets and
compare results with those.
• Reach a conclusion as to which algorithm is preferable and
find rationalization behind it.
Algorithms

8. Thank You